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In view of the complex shapes and different sizes of abrasive grains in lubricating oil, traditional lubricating oil abrasive grain detection methods have disadvantages such as poor timeliness, small detection scale, low accuracy, and non-ferromagnetic abrasive grains cannot be detected. The paper designs an aero-engine lubricating oil abrasive grain detection method based on deep learning. Based on the continuous flow microfluidic chip-based lubricant image sampling method, the lubricant image sampling system was constructed; The image enhancement method was designed, and the image data enhancement ablation experiment was carried out, the test accuracy of the YOLOv3 model and the Faster RCNN model was compared. The results show after the ablation test the detection ability of the YOLOv3 model is significantly better than the Faster RCNN model; in order to reduce the false detection rate of the YOLOv3 model after ablation, the SER algorithm is proposed to optimize the model’s inference confidence threshold. The research results show that the lubricating oil abrasive grain detection method can solve the problems in the traditional test, and under the confidence threshold of 0.35, the detection result of the YOLOv3 model can achieve a recall rate of 94.2% and an accuracy of 95.9%.